Architecture & Infra

A technical breakdown of distributed GPU orchestration, comparing Tensor Parallelism (TP, intra-node GPU matrix splitting) and Pipeline Parallelism (PP, inter-node layer partitioning) to serve massive 70B, 405B, and 671B foundation models at scale.

Speculative decoding is an inference optimization algorithm where a lightweight 'draft' model rapidly proposes candidate tokens and a larger target model verifies them in parallel in a single forward pass, increasing generation speed by 2x to 3x with zero loss in output quality.

A technical protocol comparison between Server-Sent Events (SSE), WebSockets, and WebRTC for delivering real-time streaming tokens, multimodal bi-directional data, and sub-300ms voice interactions in production AI applications.

An in-depth analysis of GPU cold-start mechanics during serverless model scaling, comparing modern inference serving runtimes (vLLM PagedAttention, TensorRT-LLM, and Triton Inference Server) and container optimization techniques.

AI load balancing distributes inference traffic across multiple model providers, API keys, and geographical regions to prevent rate-limit throttling (HTTP 429), minimize Time-To-First-Token (TTFT), and guarantee 99.99% availability.

Semantic caching uses vector embeddings and similarity thresholds to intercept and serve semantically equivalent user queries from an in-memory cache, reducing LLM API costs by up to 60% and returning responses in sub-10ms.

Streaming AI responses is an architecture where an LLM server pushes completion tokens to the client in real time via Server-Sent Events (SSE), eliminating the need to wait for the entire generation to finish.

AI inference latency is the total round-trip time required to receive responses from a machine learning model, composed of network propagation, gateway proxy overhead, Time to First Token (prefill), and decoding token throughput.

Multi-model routing is an AI architectural pattern where incoming prompts are dynamically evaluated and directed to the optimal model provider based on cost, latency requirements, capabilities, or upstream health status.

An AI Gateway is a specialized reverse proxy and orchestration layer that sits between client applications and downstream AI model providers to manage routing, rate limiting, automated failover, telemetry, and zero-retention privacy.